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Monitoring and Alerting: Databricks Data Engineer Professional Practice Questions

Exam-style questions on Monitoring and Alerting. Pick your answer, then open the explanation to see why it's right — and why the other options are wrong.

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1 Monitoring and Alerting

You want to extract lineage information from a pipeline’s event log, specifically the input datasets and the output dataset for each flow. Which event type contains this lineage-style relationship information?

  1. Acluster_resources
  2. Bupdate_progress
  3. Cuser_action
  4. Dflow_definition
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Correct answer: D

WHY D: The event log schema documents that flow_definition events define relationships in the dataflow graph and include output_dataset and input_datasets, which can be used to calculate lineage for each flow. WHY NOT B: update_progress is about the lifecycle/state of an update, not lineage. WHY NOT C: user_action describes user actions like START/CREATE, not dataset lineage. WHY NOT A: cluster_resources provides resource utilization metrics for classic compute, not data lineage.

2 Monitoring and Alerting

Your team wants a lightweight script in CI that checks whether a specific Lakeflow Job run failed overnight and posts a summary to Slack. Which statement best describes how the Databricks CLI relates to the REST APIs for this use case?

  1. AThe Databricks CLI is a wrapper around the Databricks REST APIs, so you can use it to call Jobs/Pipelines operations similarly to calling the underlying API endpoints.
  2. BThe Databricks CLI is unrelated to the REST APIs; you must use browser automation to read the Jobs UI.
  3. CThe Databricks CLI can only manage notebooks and repos; it cannot interact with Jobs or Pipelines.
  4. DThe Databricks CLI only works from within a Databricks notebook.
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Correct answer: A

WHY A: Databricks documents that the CLI wraps the REST API and exposes commands that correspond to API operations, making it suitable for automation (including monitoring/inspection flows) without requiring UI interaction. WHY NOT B: UI scraping is unnecessary; APIs/CLI are designed for programmatic workflows. WHY NOT C: The Jobs API and Pipelines API are part of the workspace REST API surface and can be used programmatically (directly or via CLI). WHY NOT D: The CLI is a local tool; it is not restricted to running inside notebooks.

3 Monitoring and Alerting

A data engineer cannot query system.query- and system.billing-related system tables from a notebook. They get a permissions error even though they can query normal Unity Catalog tables. What is the most likely missing requirement?

  1. AThey must attach the notebook to an all-purpose cluster; system tables cannot be queried from job clusters.
  2. BThey need Unity Catalog privileges to use the system catalog and select from the relevant system schemas/tables.
  3. CThey must enable Change Data Feed (CDF) on the system tables before reading them.
  4. DThey must first create an external location and storage credential for the system tables storage.
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Correct answer: B

WHY B: System tables live in the system catalog and are governed by Unity Catalog. A common cause of access errors is missing privileges such as USE CATALOG on system and SELECT on the specific schemas/tables. WHY NOT A: System tables are not restricted to only interactive clusters as a rule; permissions are the typical blocker. WHY NOT C: CDF is a Delta table feature for your data, not a prerequisite for reading system tables. WHY NOT D: External locations/storage credentials are for object storage access, not for granting access to the system catalog.

4 Monitoring and Alerting

You want to share detailed query execution information with a teammate to help debug a performance regression. You both have access to the workspace, but your teammate wasn’t present when you ran the query. What is the best way to share the detailed query profile information according to the Query Profile UI capabilities?

  1. ASend a screenshot only; the Query Profile UI does not support exporting or downloading profile details.
  2. BExport the query plan by running EXPLAIN FORMATTED and that fully replaces the need for Query Profile data.
  3. CCopy the Spark UI URL; Query Profile details cannot be shared outside of the Spark UI.
  4. DDownload the query profile JSON from the Query Profile UI and share that artifact with your teammate.
Show answer & explanation

Correct answer: D

WHY D: The Query Profile UI supports downloading the query profile as JSON, which is the most faithful way to share the detailed operator graph/metrics with someone else. WHY NOT A: Screenshots lose structured detail and the UI provides a JSON download option. WHY NOT C: Spark UI can help for Spark jobs, but Query Profile has its own export (JSON) and can be accessed via Query History/pipelines contexts. WHY NOT B: EXPLAIN is useful, but it does not include the same runtime metrics and operator timing details that the query profile captures.

5 Monitoring and Alerting

You configure a job-level notification for Failure. A task fails but is retried and eventually succeeds, and you do not receive a failure notification. What is the most accurate explanation?

  1. ADatabricks sends failure notifications only after three different tasks fail in the same run.
  2. BFailure notifications are sent only for notebook tasks; JAR and Python tasks do not support notifications.
  3. CJob-level failure notifications are not sent when failed tasks are retried; use task-level notifications to notify on every failed task attempt.
  4. DFailure notifications require configuring a webhook destination; email is not supported.
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Correct answer: C

WHY C: Databricks job notifications documentation states job-level notifications aren’t sent when failed tasks are retried. If you need a notification after every failed task attempt, you must configure task notifications instead of only job-level notifications. WHY NOT B: Notifications apply broadly to jobs/tasks, not just notebook tasks. WHY NOT A: There is no such “three task failures” rule in the notifications behavior. WHY NOT D: Email notifications are supported; webhooks/system destinations are optional alternatives.

6 Monitoring and Alerting

You need to programmatically determine whether a Lakeflow Spark Declarative Pipeline update is currently RUNNING, FAILED, or COMPLETED, and you also want to retrieve pipeline details. Which REST API surface is the best fit?

  1. AJobs API, because pipelines are always executed as jobs and pipeline state is only available through job runs.
  2. BPipelines API, because it provides endpoints to view details about pipelines and their updates.
  3. CClusters API, because pipeline status is tracked only at the cluster level.
  4. DSQL Warehouses API, because pipelines are SQL statements executed on warehouses.
Show answer & explanation

Correct answer: B

WHY B: Databricks provides a dedicated Lakeflow Spark Declarative Pipelines API (Pipelines API) to create, start, and view details about pipelines, including update/run details that expose state/progress. WHY NOT A: Jobs can run pipeline tasks, but pipeline-specific state and metadata are exposed via the Pipelines API and pipeline event log. WHY NOT C: Cluster APIs do not provide pipeline lifecycle state at the pipeline abstraction layer. WHY NOT D: Pipelines are not just SQL warehouse queries; they run as managed pipeline updates.

7 Monitoring and Alerting

You create a SQL Alert that checks whether the count of NULLs in a key column exceeds a threshold. After the scheduled run, the alert shows a status of TRIGGERED. What does TRIGGERED indicate?

  1. AThe alert query succeeded and the condition evaluated to true.
  2. BThe alert query failed to run due to missing warehouse permissions.
  3. CThe condition evaluated to false and the monitored metric is within limits.
  4. DThe alert was paused and did not execute.
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Correct answer: A

WHY A: In SQL Alerts, TRIGGERED indicates the alert condition was met (the condition evaluated true) on the most recent evaluation. WHY NOT B: Query failures are represented as ERROR, not TRIGGERED. WHY NOT C: A false condition maps to OK. WHY NOT D: Paused alerts generally do not execute; TRIGGERED is a result state from evaluation.

8 Monitoring and Alerting

You want to receive an alert when a streaming job’s backlog stays too high. Which statement best reflects how Databricks sends Streaming backlog notifications for jobs/tasks?

  1. ANotifications are sent immediately when backlog exceeds the threshold in any single measurement.
  2. BStreaming backlog notifications require enabling Delta Sharing on the target tables.
  3. CStreaming backlog notifications are only available through the Spark UI and cannot be configured in job notifications.
  4. DNotifications are sent when the average backlog over a 10-minute period exceeds the threshold, and Databricks waits 30 minutes before sending another update while backlog remains high.
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Correct answer: D

WHY D: Databricks job notifications documentation states that Streaming backlog notifications trigger when the average backlog over a 10-minute period exceeds the defined threshold, and to prevent noise, Databricks waits 30 minutes before determining whether to send another message while the backlog remains high. WHY NOT A: It is not a single-sample immediate trigger; it uses a 10-minute average. WHY NOT C: Streaming backlog is a supported notification event type in the Jobs UI notification configuration. WHY NOT B: Delta Sharing is unrelated to job streaming backlog notifications.

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